Estimation of Shear Wave Velocity Using Horizontal to Vertical Spectrum Ratio (HVSR) Inversion to Identify Faults in Pacitan

Haryono, Arif; Sungkono, Sungkono; Caesardi, M. A.; Santosa, Bagus Jaya; Syaifuddin, Firman; Widodo, Amien

doi:10.1088/1755-1315/506/1/012051

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Kondisi geologi di daerah Pacitan sebagian besar didominasi oleh batuan vulkanik seperti di Formasi Watupatok, Mandalika, Dayakan, Wuni, Jaten, Semilir, dan Nampol. Sedangkan Formasi Wonosari didominasi oleh karst, dan Formasi Arjosari serta Oyo tersusun atas kombinasi antara batuan vulkanik dan karst [5].…”

Section: Kondisi Geologiunclassified

Penentuan Kedalaman dan Sudut Kemiringan Sesar Grindulu di Pacitan Menggunakan Metode Crow Search Algorithm (CSA) pada Data Self-Potential (SP)

Haryono

Agustin²

2023

PPJ

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Self-Potential (SP) is a geophysical survey method that is relatively easy and inexpensive. Interpretation of SP data can be used for various purposes such as the detection of landslide-prone areas, exploration of various types of minerals, and identification of the parameters of a fault or crack. In this study, SP data acquisition was carried out in Tambakrejo Village, Pacitan District with a total of 102 measurement data which aims to determine the depth and dip of the Grindulu Fault. SP data acquired in the field needs to be corrected for reference, namely corrections caused by a displacement of the starting point of measurement. This data is then filtered to increase the signal-to-noise ratio (SNR) and sharpen the resulting anomalies. This filtering process is carried out using the ICEEMD (Improved Complete Ensemble Empirical Mode Decomposition) method which is a development of the EMD method. Furthermore, the SP data inversion process to obtain model parameters is carried out by utilizing the CSA (Crow Search Algorithm) method. Based on the anomaly model generated from the SP data inversion process, it can be concluded that the Grindulu Fault was identified at a distance of 803,8 meters from the starting point of measurement with depths ranging from 11,06 to 102,74 meters. Furthermore, based on distance, depth, and anomaly shape data, the dip value can be calculated. The calculation results show that the dip of the Grindulu Fault in the study area is 75.58o. Identification of the Grindulu Fault in the form of depth and dip is very important in efforts to model the fault comprehensively.

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Section: Kondisi Geologiunclassified